Stray Flux Research Articles

Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors Based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals

Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine's integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.

Stray Flux Multi-Sensor for Stator Fault Detection in Synchronous Machines

The aim of this paper is to detect a stator inter-turn short circuit in a synchronous machine through the analysis of the external magnetic field measured by external flux sensors. The paper exploits a methodology previously developed, based on the analysis of the behavior with load variation of sensitive spectral lines issued from two flux sensors positioned at 180° from each other around the machine. Further developments to improve this method were made, in which more than two flux sensors were used to keep a good sensitivity for stator fault detection. The method is based on the Pearson correlation coefficient calculated from sensitive spectral lines at different load operating conditions. Fusion information with belief function is then applied to the correlation coefficients, which enable the detection of an incipient fault in any phase of the machine. The method has the advantage to be fully non-invasive and does not require knowledge of the healthy state.

Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters

The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) are one of the most commonly used. In many industrial applications, the IM are driven by different types of starters, quite often by soft-starters. Despite rotor damages are more prone to occur in line-started motors, these kind of failures have been also reported in those ones driven by soft-starters. Related to this, the use of these type of starters may introduce some harmonic components, that could veil the magnetic flux signature of the different rotor faults. So, the aim of this study is to confirm if the Stray Flux Analysis technique maintains its reliability in these cases. Thus, this article presents the results of soft-started induction motors start-up tests, both in healthy and faulty motors. The fault components are detected by analyzing the stray flux during the starting and the study is complemented by analyzing the stray flux during the steady-state. In addition to the failure patterns, numerical indicators have been found so the identification of the failures is not only qualitative, but also quantitative. The results confirm the potential of the technique for detecting electromechanical failures in soft-started SCIMs.

Evaluation of the Damper Condition in Synchronous Motors Through the Analysis of the Transient Stray Fluxes and Currents Considering the Effect of the Remanent Magnetism

This article proposes the qualitative and quantitative analysis of stray-flux and current data under starting to detect damper faults in cylindrical rotor synchronous machines. These machines are typically employed in high power applications and their possible outages may imply huge costs for the industries or plants where they operate. The damper cage is a critical part of these machines and a potential source of catastrophic failures. However, few research works have provided feasible alternatives to monitor the condition of such element. This article analyses the viability of analyzing the electromotive force signals induced by the stray-flux in external coil sensors as well as current signals under starting to diagnose damper faults. The results obtained with laboratory machines with different levels of damper damage show that the analyses of those signals can provide very useful information for determining how the damper degrades over time. Moreover, the article studies the effect of the remanent magnetism over the viability of the approaches and provides solutions to overcome this problem. The conclusions are valuable for field engineers since, nowadays, there are few available solutions that allow monitoring the condition of such element without motor disassembly.

Convolutional Neural Networks for Automated Rolling Bearing Diagnostics in Induction Motors Based on Electromagnetic Signals

Bearing faults account for over 40% of induction motor faults, and for this reason, for several decades, much attention has been paid to their condition monitoring, through vibration measurements and, more recently, through electromagnetic signal analysis. Furthermore, in the last few years, research has been focused on evaluating deep learning algorithms for the automatic diagnosis of these faults. Therefore, the purpose of this study is to propose a novel procedure to automatically diagnose different types of bearing faults and load anomalies by means of the stator current and the external stray flux measured on the induction motor in which the bearings are installed. All the data were collected by performing experimental tests in the laboratory. Then, these data were processed to obtain images (scalograms and spectrograms), which were elaborated by a pre-trained Deep Convolutional Neural Network, modified through the transfer learning technique. The results demonstrated the ability of the electromagnetic signals, and in particular of the stray flux, to detect bearing faults and mechanical anomalies, in agreement with the recent literature. Moreover, the Convolutional Neural Network has been proven to be able to automatically discriminate bearing defects and with respect to the healthy condition.

Stray radiation energy fluxes in ITER based on a multiresonator model

Here we re-evaluate the stray radiation loads due to electron cyclotron resonance heating and diagnostic gyrotrons in ITER, as well as compute the stray radiation energy flux due to electron cyclotron emission. Microwave loads due to stray radiation were assessed in the past. We give here more comprehensive estimates, including spatial variations in the machine. We show that for the start-up phase in the First Plasma campaign the threshold of 100 kW/m2, which diagnostics have to be able to withstand, is not exceeded. For the later campaigns, the stray radiation energy flux during start-up and burnthrough will exceed 450 kW/m2. Stray radiation from electron cyclotron emission and from a diagnostic 60 GHz gyrotron can contribute to more than 140 kW/m2 combined.

The interaction of parallel and inclined planar rarefied sonic plumes—From free molecular to continuum regime

The interaction between rarefied vapor plumes can cause shocks and consequently distinct peaks in mass flux which produce undesirable non-uniformities. To evaluate the impact of shock formation, we study pairs of interacting planar plumes, varying the degree of rarefaction and general geometric parameters, namely, the nozzle-separation-distance and the mutual plume inclination. To consider the extremes of rarefaction, we give the analytic solution for free molecular flow and simulate the inviscid continuum solution using an approximate Riemann solver. In the transitional flow regime, direct simulation Monte Carlo is applied. To detect the shock location, we make use of the Method of Characteristics. We conclude that, although the rarefied flow regime physically lies in between the free molecular and the inviscid continuum flow regimes, the peak value of mass flux in the transitional flow regime exceeds both the one of free molecular flows and the one of inviscid continuum flows (the latter by ≈10%). Rarefied flow exhibits a broader, but weaker secondary expansion after the shock than continuum flow. For planar jet interaction, the occurrence of the shock is rather insensitive to nozzle separation distance. Despite the intuitive expectation that inclining the plumes away from each other would lead to shock reduction and thus give a more uniform mass flux, the opposite is the case: Inclining the plumes toward each other leads to a stronger shock, but also to a stronger expansion, thus producing a more uniform mass flux with less stray mass fluxes.

Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Fast Hybrid Approach for Calculation of Losses in Outer Packages of Transformer Core Due to Perpendicular Stray Flux

Stray flux in a transformer enters the outer packages of a core perpendicularly and induces significant eddy currents that cause ohmic losses. Local thermal conditions near the core are affected by these losses that are dissipated as heat and can cause local overheating in the oil near the outer packages. Stray losses in the outer packages should be considered during the power transformer design process, and it is, therefore, important to model them properly. This type of loss can be solved using 3-D FEM; however, that approach is time-consuming, which makes it impractical for the loss calculation in real time. Another approach is 2-D modeling, which is faster, but it is limited to axisymmetric and planar geometries only. Due to its complex geometry, a transformer cannot be modeled using a single 2-D model. Instead, each quantity of interest, such as a magnetic field or eddy currents, can be obtained from a suitable model. These models include certain simplifications, which should not affect the accuracy of the results. In this article, a hybrid approach that utilizes various 2-D models for the calculation of stray losses in the outer packages is proposed. The advantage of the proposed methodology is in obtaining the same level of accuracy while considerably reducing the calculation time when comparing to the 3-D FEM approach. The estimation of the temperature rise of the outer packages, based on the calculated losses, will be a part of further research.

Stray Flux-Based Rotation Angle Measurement for Bearing Fault Diagnosis in Variable-Speed BLDC Motors

Angle of rotation is a key parameter in motor fault diagnosis under varying speed conditions, and is usually measured by an optical encoder. However, the use of encoders is intrusive and in many scenarios its signal is difficult to access due to technical or commercial reasons. In this study, a novel rotation angle measurement method based on stray flux analysis is proposed and applied to bearing fault diagnosis of brushless direct-current (BLDC) motors. The measurement accuracy of the proposed method is comparable to that from an encoder. The developed method is flexible, noninvasive, and nondestructive. It is easy to implement and eliminates the need for long cables and access of the motor control system. The proposed method can be extended to the diagnosis of motor electrical and drive faults. If implemented with an Internet of Things (IoT) or a hand-held device, it can further improve the reliability of sensorless motor drive systems in industrial automation so as to meet Industry 4.0 requirements.

Orbit Analysis From a Stray Flux Full Spectrum for Induction Machine Fault Detection

Magnetic flux sensors in particular, combined with appropriate signal processing techniques, create excellent opportunities for extracting significant information for fault diagnosis of induction motors. Recent research has opened the possibility of simultaneously obtaining magnetic flux signals displaced approximately 90° in time. This makes it possible to correlate the stray magnetic flux from different transducers. In this scenario, the Full Spectrum technique, widely used for vibration signals, can become a useful tool for displaying the relationship between different magnetic flux components in the frequency domain. Taking this into account, this work proposes the use of the filtered orbit, modified and obtained from the Full Spectrum, for interpreting the magnetic flux signals. This was done for the motor working under inter turn short circuits (three, six and nine turns short), unbalanced voltage fault or without fault conditions at different loads. Two characteristic frequencies of faults were considered, 180 Hz and 420 Hz. The experimental results showed the viability of the application of the technique and made it possible to identify the severity and the distinction of the faults studied as the orbital area and the direction of rotation were changed due to the presence of the fault. For example, for the incipient failure (three turns short), considering all loads, the orbit area at 180 Hz increased no less than 92% and the unbalanced voltage by no less than 885%. Additionally, the pattern of the orbit rotation at 420 Hz, was also affected by the operating condition of the motor under unbalanced voltage.

Eddy Current Loss in Grain-Oriented Steel Laminations Due to Normal Leakage Flux

Leakage flux penetrating laminated iron cores in power transformers and large generators induces eddy current and local loss. Due to the strong magnetic anisotropy of the lamination structure, the penetrating flux tends to saturate the lamination in its plane, even when the incident stray flux density is low. Therefore, the combined effect of anisotropy and nonlinearity has a great impact on the eddy current distribution and the associated power losses. Moreover, the incident normal flux often interacts with the main flux, where the phase angle between the two fluxes may play a significant role. A measurement device is developed to emulate the actual leakage flux in a steel lamination and the power losses are measured at the flux densities of various magnitudes and phases. The measurement results are compared and interpreted by the results obtained from a finite-element analysis, where the homogenization approach for material modeling is implemented, taking the combined effect of magnetic anisotropy and the saturation into account.

Detection of simultaneous mechanical faults in 6‐kV pumping induction motors using combined MCSA and stray flux methods

The popularity of stray and air-gap flux monitoring methods is increasing. This trend is justified by several advantages of such methods over the stator current monitoring that has been demonstrated for electrical fault detection in the induction and synchronous machines. However, the use of the magnetic flux for mechanical faults detections has not drawn this much attention, while in industry, the vibration analysis continues to be popular. This study comes to bridge this gap via the detection of mechanical faults of 6-kV induction motors in a pumping station. The diagnostic procedure mainly involves the stator current and stray flux monitoring, and harmonic index analysis. The localisation of the fault has been made possible via oscillometer readings. It will be shown that mechanical faults have a very different impact on the stator current and the flux signals, while the flux is not sensitive to the bearing fault mechanisms.

Design and Experimental Analysis of 166 kW Medium-Voltage Medium-Frequency Air-Core Transformer for 1:1-DCX Applications

The galvanic isolation of solid-state transformers (SSTs) is typically realized with a medium-frequency (MF) magnetic-core transformer (MCT). Previous demonstrations indicate that achieving highly power-dense and lightweight MCTs imposes several challenges on the design because of stringent requirements related to insulation and cooling. This work investigates the achievable efficiency of an optimized 166 kW/7 kV air-core transformer (ACT), which is a core part of a dc transformer (DCX), i.e., an unregulated dc–dc SST with a voltage scaling defined by the transformer turns ratio. The ACT features relatively low complexity of the construction, comparably high coupling values, and high efficiency. Modeling, optimization, and construction of the realized ACT are explained and guidelines regarding insulation, cooling, and shielding of the magnetic stray flux are discussed in detail. Furthermore, the prototype is experimentally validated to demonstrate its full functionality. In the investigated DCX, which is based on a series resonant converter (SRC) topology, the realized ACT is found to achieve a full-load efficiency of 99.5% and an unprecedented gravimetric power density of 16.5 kW/kg. With the use of 10 kV SiC MOSFETs, the complete DCX is estimated to reach an efficiency of 99% at 166 kW output power.

Effect of Magnets Asymmetry on Stray Magnetic Flux Based Bearing Damage Detection in PMSM

In this paper, it is shown that stray magnetic flux is a highly reliable condition monitoring variable which is rich in motor health related information and it is superior to motor current. It is well-known that the magnetic field distribution in permanent magnet (PM) rotors exhibit asymmetry due to magnet manufacturing process. It is shown that, in addition to the clear characteristic bearing signatures, the magnet imperfections induce multiple new bearing fault signatures in the stray magnetic flux. Different than current, the signatures in stray flux are more immune to noise, consistent and independent of load and speed. It is also shown that the stray flux-based detection method can locate the faulty bearing. To justify the proposed approach, comparative simulations and experiments are carried out on a surface mounted PM synchronous motor (PMSM) and an interior permanent magnet motor (IPM) with outer raceway bearing fault and different degree of magnet field asymmetry.

Geomagnetic induced current modelling and analysis on high voltage power system

Geomagnetically induced current (GIC) has become a significant concern that can affect the electrical power grid by causing a half-cycle saturation of power transformers. This saturation leads to an increase in even and odd harmonic distortions and reactive power losses of transformers, which may consequence in improperly triggering relays, tripping reactive power (VAR) compensators and shunt capacitor banks. Also, these reactive losses, harmonic, and the stray flux resulted due to this saturation may lead to overheating windings and cores of power transformers and generators and hence blackout. In this paper, GIC analysis has been conducted on a modified IEEE-18 bus test system by using Power System Simulator for Engineering (PSS/E) and Power System Computer-Aided (PSCAD/EMTDC) software. The simulation results have been obtained by considering a worst-case scenario of geomagnetic disturbance (GMD) by applying uniform induced electric fields with values of 10 V/km and 20 V/km at different directions with and without GIC blocking devices. Also, the impact of grounding resistances of the substations on calculated GIC due same mentioned induced fields has been investigated. The results show that the highest total reactive power losses across the system are obtained due to related induced electric fields at 120° and 300° storm angle. After the connection of GIC blocking devices to the substations, these reactive losses have drastically reduced. In addition, simulation results of the same test system by using PSCAD platform are obtained to investigate hysteresis and harmonic results during system operation and the presence of GIC.

Transient Roebel Bar Force Calculation in Large Salient-Pole Synchronous Machines

The radial and tangential force components acting on each sub-conductor of a Roebel bar in a large generator lead to mechanical vibration of the stator bars in the slot. This double-frequency vibration results in additional mechanical stress on fixation materials in the slot and causes insulation deterioration and looseness of stator bars. Particularly during short circuits, the stator bars are subjected to immense radial forces. Understanding the origin of these vibrations and developing models to anticipate them is essential to increase the reliability and expected life time of large generators. This paper presents an analytical method for the estimation of Roebel bar forces during symmetrical and asymmetrical short circuits. For this purpose, an analytical calculation method is developed to estimate the transient three-phase currents during different short circuits. These currents induce tangential and radial flux density components along the slot width and length, respectively. An analytical approach is introduced to estimate these flux densities as a function of the slot length. Subsequently, an analytical method for calculating the Roebel bar forces considering three-dimensional transpositions is developed and verified with finite element calculations. Beyond the state of the art, the impact of the rotor field on the radial and tangential components of the stray fluxes are analytically estimated. The result is a comprehensive analytical tool for the estimation of Roebel bar forces during different short circuits in large generators.

Detection of Field Winding Faults in Synchronous Motors via Analysis of Transient Stray Fluxes and Currents

The detection of rotor failures in synchronous motors is a matter of primordial interest in many industrial sites where these machines are critical assets. However, due to the particular operation of these motors, most conventional techniques relying on steady-state analysis, commonly used in other electric machines, are not applicable to such motors. In this context, it has been recently proven that the analysis of different quantities under transient operation of the motor and, more specifically, under motor starting can provide crucial information for the diagnosis of many faults. This work proposes the time-frequency analysis of stray fluxes and currents to detect field winding faults in synchronous motors. The potential consequences of this fault can be catastrophic for the motor integrity, so that the detection of its presence in its early stages can be of critical importance for the industry. The results included in this paper prove the usefulness of the transient analysis of such non-invasive quantities not only to detect the presence of the field winding fault but also to set a starting point to determine its severity.

Axial Stray Flux Sensor Proposal for Three-Phase Induction Motor Fault Monitoring by Means of Orbital Analysis

This paper proposes a new form of stray flux sensor, placed on the inner side of the machine rear end plate, in order to monitor the presence and evolution of some most common faults in three-phase induction motors. The fault indicator is based on analysis of orbits built from time flux signals collected by the sensor. The effectiveness of this methodology was checked considering three types of faults under three different load conditions: incipient inter-turn short circuits, unbalanced voltage supplies and misalignment, all with 0%, 50% and 100% loads. The experimental results showed a viability of the technique for diagnosing and monitoring of induction motors. This can be adapted and used in predictive maintenance in industry.